Multimodal ultrasound assessment for monitoring keloid severity and treatment response
Bibliographic record
Abstract
The current understanding and a standardized assessment or treatment guidelines for keloids are not fully established, highlighting the need for an objective method to gauge keloid severity and treatment outcomes. This study investigated the clinical utility of multimodal ultrasound, integrating Shear Wave Elastography (SWE) and Angio planewave ultrasensitive imaging (AP), to assess keloid severity and treatment responses in 58 keloids across 31 patients. Keloids were categorized into mild, moderate, and severe based on Vancouver Scar Scale (VSS) scores. The results revealed significant differences in keloid thickness, elasticity parameters, and blood flow levels among severity groups, with the AP technique demonstrated superior sensitivity in detecting keloid microcirculation. Additionally, the study evaluated the therapeutic response to Strontium-90 Yttrium-90 isotope applicator treatment in 28 keloids, categorizing them into 13 good responders and 15 poor responders based on improvements observed in their VSS scores. Good responders demonstrated marked improvements post-treatment, including significant flattening of the keloids, decreased stiffness, and normalization of blood flow levels. In contrast, poor responders exhibited minimal changes in keloid thickness, stiffness, and blood flow signals following treatment. These findings underscore the effectiveness of multimodal ultrasound in evaluating treatment responses in keloid management. In conclusion, multimodal ultrasound, focusing on SWE and AP modalities, offers a promising tool for comprehensive assessment, with potential to enhance keloid evaluation and track treatment responses across varying therapeutic interventions, thereby facilitating optimized clinical management and guiding personalized treatment. The study was successfully registered on ClinicalTrials.gov on 12/09/2023, with the Identifier NCT06034587.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".